A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus

In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) va...

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Veröffentlicht in:	PloS one Jg. 15; H. 11; S. e0242028
Hauptverfasser:	Haga, Hiroaki, Sato, Hidenori, Koseki, Ayumi, Saito, Takafumi, Okumoto, Kazuo, Hoshikawa, Kyoko, Katsumi, Tomohiro, Mizuno, Kei, Nishina, Taketo, Ueno, Yoshiyuki
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 05.11.2020 Public Library of Science (PLoS)
Schlagworte:	Aged Algorithms Analytical methods Antiviral agents Antiviral Agents - therapeutic use Antiviral drugs Artificial Intelligence Bayes Theorem Bayesian analysis Big Data Biology and Life Sciences Computer and Information Sciences Datasets Decision trees Diagnostic systems Discriminant analysis Drug Therapy, Combination - methods Female Gastroenterology Gene sequencing Genetic aspects Genetic Variation - genetics Genome, Viral - genetics Genomes Genomic analysis Health aspects Hepacivirus - drug effects Hepacivirus - genetics Hepatitis Hepatitis C Hepatitis C - drug therapy Hepatitis C - virology Hepatitis C virus Humans Identification and classification Learning algorithms Machine Learning Male Medicine Medicine and health sciences Model testing Multilayers Neural Networks, Computer Next-generation sequencing Patients Performance evaluation Physical Sciences Prediction models Research and Analysis Methods Ribonucleic acid RNA RNA, Viral - genetics Statistical analysis Statistics Support Vector Machine Support vector machines Sustained Virologic Response Testing Training University faculty Viruses Whole genome sequencing Japan
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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